The Place Premium
Lant Pritchett
(paper with Michael Clemens, CGD and Claudio
Montenegro, World Bank)
LEP Lunch/Development Seminar
Sept 29, 2008
Outline of the presentation
• Empirical estimates of wages differences for
observationally equivalent workers on opposite
sides of the US Border
• Addressing the issue of migrant self-selection
–
–
–
–
–
Simulation with residuals
Data from Latin America
One true experiment
Comparison with macro growth accounting
Experiences with spatially distinct but open borders
• Comparisons of (adjusted) wage gaps with other
“similar” numbers (wage discrimination, etc.)
Drilling down through wage
surfaces
ln(w)
USS (born, educated) workers
In USA
ŵ USA ( X )
R0 
ŵ Bol ( X )
Bolivian (born, educated) workers
In USA
Bolivian (born,
educated) workers in
Bolivia
X (e.g. education, age)
New collection of data sets
• 2,015,411 formal-sector wage-earners in
43 countries
• 42 different countries wage surveys—of
wage earners
–
–
–
–
–
–
Wages (converted to monthly, PPP)
Country of birth
Amount and country of schooling
Age/experience
Gender
Rural/urban
The “formal sector” is a big
Issue for the poor African
Countries In the sample
1000
2000
4000
Comparison of our wage survey results
with labor value added per worker
We just drop
Honduras
USA
250
500
HND
125
SLE
ZAF
CRI
CHL
TUR
PRY
URY
DOM
BLZ
MEX
JAMMAR PAN
COL
NIC
THA
GUY
PER BRA
BOL
PHL
JOR
GTM
TCD
ECU
UGA
BGD
VEN
KHM
IDN LKA
VNMCMR IND
EGY
PAK
ETH NGA YEM
NPL
ARG
62.5
GHA
HTI
62.5
125
250
500
1000
2000
PPP Labor income, 0.65 share, log scale
45 deg. line
Cubic fit w/o HND
4000
Combine with PUMS US Census
• Wages of individuals, with country of birth
and age at arrival plus
– Schooling
– Age
– Sex
– Urban/rural residence
Results of the wage surface drilling: foreign born, foreign
educated (late arrivers), high school or less educated, 35
year old, males, in urban areas in USA vs home
Comparing foreign born, foreign educated in US to in
home
Ratio of US to
country
wages
Predicted annualized wages (2000 PPP)
R0
In US
In Home
Absolute gap
Mean
7.3
5.1
$20,764
$5,352
$15,411
Median
6.2
4.1
$19,972
$4,675
$15,438
Nigeria (2nd highest)
13.5
14.9
$18,394
$1,238
$17,155
Haiti
23.5
10.3
$17,428
$1,690
$15,738
India
10.9
6.3
$23,024
$3,684
$19,340
Philippines
6.2
3.8
$18,436
$4,820
$13,615
Brazil
5.0
3.8
$23,725
$6,302
$17,423
Mexico
3.8
2.5
$17,650
$6,971
$10,679
Dom Rep. (lowest)
3.3
2.0
$17,897
$8,984
$8,912
Selected Countries of Interest
20
Ro
0
Yemen
Nigeria
Egypt
Haiti
Cambodia
Sierra Leone
Ghana
Indonesia
Pakistan
Venezuela
Cameroon
Vietnam
India
Jordan
Ecuador
Bolivia
Sri Lanka
Nepal
Bangladesh
Uganda
Ethiopia
Guyana
Philippines
Peru
Brazil
Jamaica
Chile
Nicaragua
Panama
Uruguay
Guatemala
Colombia
Paraguay
South Africa
Turkey
Argentina
Mexico
Belize
Thailand
Costa Rica
Morocco
Dominican Rep.
Estimates of R0 (predicted wages of observationally
workers across the US border) for 42 countries with
95% confidence intervals
30
25
38/42 can reject bigger than 1.5
32/42 cannot reject bigger than 4
15
10
5
All kinds of comparability issues:
but the biggest is PPP
• Gross versus net
• Inclusion of benefits (inkind, entitlements) or not
• Valuation of workplace
amenities (e.g. safety
regulation)
• But we suspect the
biggest is imputation of
the location of
consumption (in US
versus home)
• Remittances about 20 percent
For Mexicans
• Remittances/savings about 60
For Philippines overseas workers
• Think “optimal” savings of temporary
worker
Estimates of R0 at various
fractions at PPP versus
official exchange rates
100%
(base)
80%
40%
0%
Mean
5.1
5.8
8.3
16.4
Median
4.1
4.9
7.2
13.9
How much of the observed wage differentials of
observationally equivalent workers represent
border restrictions vs. selection or home
preference?
• Six different methods/data for examining
wage selection, all of which suggest our
predicted mean wage ratios of
observationally equivalent workers overstate wage ratios of equal intrinsic
productivity workers by between 1 and 1.4.
The question of selection on
unobservable
• Our estimates of compare what those who moved to US make
versus what those who are observationally equivalent make in
home.
• But those who did move might have made more than the o.e.
counter-parts so R0 overstates the gain
• We are not talking about the upper end but the low skill end—people
making 10$/hour
• Not obvious that there is positive self-selection on unobservable
productivity in the home market—theory is that people would
maximize the gain from moving if either:
– productivity is a market match phenomena (e.g. having an uncle with a
good business), or
– Individually differential obstacles (e.g. family unification visas)
then one might expect zero or negative selection.
India
0.6
0.8
R0 compares means
0.0
0.2
0.4
Could compare to other
percentile of the home
distribution of unobservables,
e.g. 70th
0
5
10
Component plus residual from ln(wage) regression
USA born, USA res, USA educ
IND born, IND res, IND educ
IND born, USA res, USA educ
IND born, USA res, IND educ
15
1st approach: Wage ratios under various
assumptions about where in the home distribution
of unobservables migrants came
50th
Median across countries
70th
4.5
90th
95th
3.4
2.1
1.6
1.34
2.20
2.85
10.34
6.92
4.24
3.49
Haiti
8.76
4.08
1.34
0.86
India
7.05
5.16
3.28
2.6
Philippines
3.77
2.73
1.76
1.44
Brazil
4.23
3.2
2.03
1.6
Mexico
3.32
2.44
1.57
1.24
Dominican Rep.
2.95
2.26
1.71
1.07
Ratio to assumption of
50th
Selected Countries of Interest
Nigeria
9
8
7
Re
0
Egypt
Yemen
Nigeria
Sierra Leone
Jordan
Venezuela
Indonesia
Pakistan
Vietnam
India
Nepal
Cameroon
Cambodia
Ecuador
Bangladesh
Sri Lanka
Ghana
Guyana
Bolivia
Jamaica
Brazil
Chile
Turkey
Ethiopia
Uganda
Philippines
Panama
Peru
Nicaragua
Colombia
Paraguay
Uruguay
Belize
Argentina
Guatemala
Mexico
Costa Rica
South Africa
Morocco
Haiti
Thailand
Dominican Rep.
Wage ratios of equally productive workers at
various assumptions of source of migrants in
distribution of unobservables
10
30th
50th
70th
90th
95th
6
5
4
3
2
1
2nd Approach: Data from the Latin
American Migration Project (LAMP)
• Tracks migrants from seven Latin
American countries and does surveys in
their origin localities of non-migrants
• Wage histories of migrants including last
wage before migrating
• Compare wages of migrants before
moving and non-migrants, with distribution
of residuals
0.6
0.8
Distribution of the unobserved component on
wages (residuals) in home for migrants and nonmigrants: Mexico
0.0
0.2
0.4
Mean migrant at 53rd
Percentile of non-migrants
0
2
4
6
8
ln(wage)
Migrant in home
Non-migrant in home
10
Mexico
Actual distribution
Of residuals for Mexico
So we can compute 50th
Of movers to 53rd of home
0
5
10
Component plus residual from ln(wage) regression
USA born, USA res, USA educ
MEX born, MEX res, MEX educ
MEX born, USA res, USA educ
MEX born, USA res, MEX educ
15
0.4
Distribution of the unobserved component on
wages (residuals) in home for migrants and nonmigrants: Haiti
0.0
0.1
0.2
0.3
Mean migrant at 61st
Percentile of non-migrants
2
4
6
8
10
ln(wage)
Migrant in home
Non-migrant in home
12
0.8
0.0
0.2
0.4
0.6
Haiti
-5
0
5
10
Component plus residual from ln(wage) regression
USA born, USA res, USA educ
HTI born, HTI res, HTI educ
HTI born, USA res, USA educ
HTI born, USA res, HTI educ
15
México
Guatemala
Nicaragua
Costa Rica
Dominican
Rep.
Haití
Perú
Typical migrant percentile in distribution of non-migrants' unobserved component of wages
Mean migrant:
53
50
54
58
51
61
69
Median migrant:
49
50
50
50
50
64
62
Ratio of migrant home wage to non-migrant home wage, conditional on observables =
exp(βmigrant)
1.07
1.00
1.10
1.19
1.06
1.46
1.42
US wage (our
data)
1471
1553
1561
1606
1491
1452
1714
Non-migrant
wage (our
data)
581
529
443
775
749
141
452
Ro
2.53
2.94
3.52
2.07
1.99
10.31
3.79
Re
2.37
2.93
3.19
1.74
1.89
7.07
2.67
Ro/Re
1.07
1.00
1.10
1.19
1.06
1.46
1.42
Results from 7 countries
• The medians of the migrant and non-migrants
are exactly the same for 5 of the 7—the
selection is mostly an upper tail thing
• Using the means to adjust out Ro estimates
lowers them by a ratio of between 1 (no
adjustment for Guatemala) to 1.46 (Haiti)
• In no country is the typical migrant from as high
as the 70th percentile of non-migrants (which,
from table above, implies an adjustment of 1.34
using the actual residuals data).
3rd Approach: Comparison with
experimental estimates of wage effects
• Movers from Tonga to New Zealand
chosen from applicants based on a lottery
• OLS wage ratio: 6.14 (chosen versus all
stayers)
• Experimental wage ratio: 4.91 (foreign
wages of randomly selected chosen
versus home wages of applicants).
• Bias from not correcting for selection:
6.12/4.91=1.25
4th Approach: Comparison to macro growth decomposition
(Hall and Jones)
Hall and Jones estimates
R
estimates,
Ratio of USA
A and K
to country
A and K
Ratio of
USA A to
country
A
Ratio wage
based
estimates
to macro
accounting
Median
3.82
3.07
2.44
1.25
Average
5.11
3.69
2.71
1.39
Average without four
outliers
4.53
3.92
2.90
1.16
5th approach: Use comparisons of average
wages of observationally equivalent in home
and foreign (allowing for country specific
schooling)
• Doesn’t involve movers at all—so should
understate the marginal mover if there is positive
selection.
• In fact, these are larger than bilateral estimates
• But one has to correct for the quality of
schooling as S in Bolivia is not S in USA
• Under various plausible adjustments of S
“evaporation” suggest selection at most
increases R0 by factor of 1.2
6th approach: wage ratios in spatially
distinct but legally integrated labor
markets: Puerto Rico
5
4
Guam=1.36
3
2
1.3
1
0
1.4
1.4
1.5
1.6
1.8
1.8
0
1
2
3
4
5
When borders were open wage ratios above 2
caused massive mobility, leading to wage
convergence
1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930
Germany
Ireland
Norway
Great Britain
Italy
Sweden
Shall I compare thee to a summer’s
day…thou are much bigger
• Wage discrimination—comparing wage
discrimination against disfavored social groups
within borders to consequence of local of
birth/citizenship/market access based wage
differentials
• Border differentials in prices of goods or capital
• Impacts of poverty programs
Our average cross-border wage differential
(5.1) is larger by a factor of 3 than racial
discrimination in the US in 1939
5
Using our wage data
we can estimate the
largest discrimination
against females in the
world, Pakistan, 3.1
4
3
2
1.1
1
0
1.1
1.3
1.4
1.4
1.4
1.6
1.9
Using historical data
one can estimate the
gap between marginal
product (rental price)
and subsistence wage
for 19th century North
American slaves:
around 3.8
Estimates of the remaining price
gaps across countries
Mean percentage absolute value
difference in producer prices across
goods
60
50
40
30
20
10
0
Source: Bradford and Lawrence, 2004
Canada-USA
Germany-USA
UK-USA
Japan-USA
Combination of small price gaps and large wage
gaps implies the estimated gains from even minor
relaxations in labor mobility are big relative to the
largest gains in remaining trade liberalization
`
305
Billions
Doubling net ODA
Net gains to developing
countries from
liberalization in Doha
round
79.5
86
Source: Winters et al 2004
Value of welfare gains to
current developing country
residents (including gains
to movers)from 3% of
OECD labor force increase
Comparing estimated gains from anti-poverty
interventions in poor countries to wage differences
Intervention
Country
Present-value
lifetime
income
increment
due to
intervention
(US$ at
PPP)
Annual wage
difference of
observationally
equivalent
male low skill
worker
Weeks of US work
equivalent to lifetime
NPV of intervention
Microcredit
Bangladesh
700
$14,891
2.4
Antisweatshop
Indonesia
2,700
$17,478
8.0
Additional year
of
schooling
Bolivia
2,250
$15,455
8.0
Deworming
Kenya
71
$16,265
0.2
Conclusion
• Massive gaps in wages between observationally
equivalent workers in 42 poor countries—average 5.1,
median 4.1--$15,000 per year (PPP)
• The bulk of the evidence suggests that the self-selection
might cause these to overstate gains from movement of
unskilled workers by a modest amount (scale back by
between 1 and 1.4)
• These make the wage differentials across borders:
– Bigger than any wage discrimination
– Bigger than any price distortion due to borders
– Bigger than any poverty impact
by factor multiples (if not orders of magnitude)